Switch to default timeout for cache

server/app/app.py

@@ -13,7 +13,7 @@ from .rest_api.rest import get_api_resources
 REACTIVE_LIMIT = 1_000_000
 
 app = Flask(__name__)
-cache = Cache(app, config={"CACHE_TYPE": "simple"})
+cache = Cache(app, config={"CACHE_TYPE": "simple", "CACHE_DEFAULT_TIMEOUT": 860000})
 Compress(app)
 CORS(app)
 

server/app/scanpy_engine/scanpy_engine.py

@@ -71,7 +71,7 @@ class ScanpyEngine(CXGDriver):
                     cell_idx = np.logical_and(cell_idx, key_idx)
         return self.data[cell_idx, :]
 
-    @cache.memoize(864000)
+    @cache.memoize()
     def metadata_ranges(self, df=None):
         metadata_ranges = {}
         if not df:
@@ -91,7 +91,7 @@ class ScanpyEngine(CXGDriver):
                 }
         return metadata_ranges
 
-    @cache.memoize(864000)
+    @cache.memoize()
     def metadata(self, df, fields=None):
         """
          Gets metadata key:value for each cells
@@ -105,7 +105,7 @@ class ScanpyEngine(CXGDriver):
             metadata[idx]["CellName"] = metadata[idx].pop("cell_name", None)
         return metadata
 
-    @cache.memoize(864000)
+    @cache.memoize()
     def create_graph(self, df):
         """
         Computes a n-d layout for cells through dimensionality reduction.
@@ -117,7 +117,7 @@ class ScanpyEngine(CXGDriver):
         normalized_graph = (graph - graph.min()) / (graph.max() - graph.min())
         return np.hstack((df.obs["cell_name"].values.reshape(len(df.obs.index), 1), normalized_graph)).tolist()
 
-    @cache.memoize(864000)
+    @cache.memoize()
     def diffexp(self, cell_list_1, cell_list_2, pval, num_genes):
         """
         Computes the top differentially expressed genes between two clusters
@@ -164,7 +164,7 @@ class ScanpyEngine(CXGDriver):
             },
         }
 
-    @cache.memoize(864000)
+    @cache.memoize()
     def expression(self, cells=None, genes=None):
         """
         Retrieves expression for each gene for cells in data frame